Static GTI has been originally addressed from a statistical viewpoint and only in the past decade under a graph signal processing (GSP) framework [1]}, in which different assumptions are made on how the data are coupled with the unknown topology; see [2]}, [3]} for a tutorial. Only recently, dynamic versions of the static counterparts have been proposed. For instance, [4]}, [5]} learn a sequence of graphs by enforcing a prior (smoothness or sparsity) on the edges of consecutive graphs; similarly, the work in [6]} extends the graphical Lasso [7]} to account for the temporal variability, i.e., by estimating a sparse time-varying precision matrix. In addition to these works, the inference of causal relationships in the network structure, i.e., directed edges, has been considered in [8]}, [9]}. See [10]} for a review of dynamic topology inference approaches.
